%0 Journal Article %J Nature Human Behavior %D 2017 %T Rational quantitative attribution of beliefs, desires, and percepts in human mentalizing %A Chris Baker %A Julian Jara-Ettinger %A Rebecca Saxe %A Joshua B. Tenenbaum %K Human behaviour %K Social behaviour %X
Social cognition depends on our capacity for ‘mentalizing’, or explaining an agent’s behaviour in terms of their mental states. The development and neural substrates of mentalizing are well-studied, but its computational basis is only beginning to be probed. Here we present a model of core mentalizing computations: inferring jointly an actor’s beliefs, desires and percepts from how they move in the local spatial environment. Our Bayesian theory of mind (BToM) model is based on probabilistically inverting artificial-intelligence approaches to rational planning and state estimation, which extend classical expected-utility agent models to sequential actions in complex, partially observable domains. The model accurately captures the quantitative mental-state judgements of human participants in two experiments, each varying multiple stimulus dimensions across a large number of stimuli. Comparative model fits with both simpler ‘lesioned’ BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model.
%B Nature Human Behavior %V 1 %8 03/2017 %G eng %U http://www.nature.com/articles/s41562-017-0064 %N 0064 %R doi:10.1038/s41562-017-0064